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SEVEN RTX 3090S IN A WATER TANK FOR AI SERVER it is a private AI server with the power bill moved into your room. not a clean Mac mini. not a quiet box under a monitor. loose vertical GPUs sit inside a transparent tank. bubbles rise through distilled water. ALLIED CONTROL is printed on the side. it looks closer to a lab accident than a normal workstation. but the logic is obvious: seven RTX 3090s = seven 24GB cards. that is the used-market shortcut for people who want local inference without paying cloud tax on every run. put Ollama, llama.cpp, vLLM, Open WebUI, Tailscale, Qwen, DeepSeek, or Llama on top. now the box can handle client files, code agents, scraping jobs, evals, transcription, and boring overnight work. not because it beats frontier cloud models. because it changes the bill shape. no rate limit. no per-token anxiety. no sensitive client context leaving the building. no monthly stack quietly turning into rent. the ugly part is physical. seven 3090s can pull serious power, dump serious heat, and punish lazy cooling. distilled water is the weird visual, not a setup tip. real immersion rigs live or die on coolant chemistry, insulation, pumps, maintenance, and whether the room can handle the heat. local AI PCs are becoming less like gaming builds and more like small private data centers. the early question is not: can it run ChatGPT? it is: what work is repetitive, private, expensive in the cloud, and worth owning in hardware?

SEVEN RTX 3090S IN A WATER TANK FOR AI SERVER it is a private AI server with the power bill moved into your room. not a clean Mac mini. not a quiet box under a monitor. loose vertical GPUs sit inside a transparent tank. bubbles rise through distilled water. ALLIED CONTROL is printed on the side. it looks closer to a lab accident than a normal workstation. but the logic is obvious: seven RTX 3090s = seven 24GB cards. that is the used-market shortcut for people who want local inference without paying cloud tax on every run. put Ollama, llama.cpp, vLLM, Open WebUI, Tailscale, Qwen, DeepSeek, or Llama on top. now the box can handle client files, code agents, scraping jobs, evals, transcription, and boring overnight work. not because it beats frontier cloud models. because it changes the bill shape. no rate limit. no per-token anxiety. no sensitive client context leaving the building. no monthly stack quietly turning into rent. the ugly part is physical. seven 3090s can pull serious power, dump serious heat, and punish lazy cooling. distilled water is the weird visual, not a setup tip. real immersion rigs live or die on coolant chemistry, insulation, pumps, maintenance, and whether the room can handle the heat. local AI PCs are becoming less like gaming builds and more like small private data centers. the early question is not: can it run ChatGPT? it is: what work is repetitive, private, expensive in the cloud, and worth owning in hardware?

591,487 görüntüleme

AGENT ARCHITECTURE ROUTES WORK. IT DOES NOT REMEMBER WORK. THAT GAP IS WHY YOUR LOOP KEEPS FIXING THE SAME BUG TWICE. these are two different engineering problems. every agent that silently drifts is missing one of them. architecture answers what runs. harness → loop → graph. it defines the tools, the retries, the branching routes, the approval gates. context ops answer what the run knows. write → read → compress → isolate. it defines what gets saved between attempts, pulled in on read, summarized on overflow, and split across sub-agents. for two months i believed a solid harness plus a verifier loop was enough. my coding agent kept re-discovering the same test failure across retries. the loop was working. it just had nowhere to write what it had already learned. here is the decision rule: if your agent forgets across restarts, add write and read. if it stalls on long tasks, add compress. if two sub-agents step on each other, add isolate. architecture without context ops is a well-routed system with amnesia.

AGENT ARCHITECTURE ROUTES WORK. IT DOES NOT REMEMBER WORK. THAT GAP IS WHY YOUR LOOP KEEPS FIXING THE SAME BUG TWICE. these are two different engineering problems. every agent that silently drifts is missing one of them. architecture answers what runs. harness → loop → graph. it defines the tools, the retries, the branching routes, the approval gates. context ops answer what the run knows. write → read → compress → isolate. it defines what gets saved between attempts, pulled in on read, summarized on overflow, and split across sub-agents. for two months i believed a solid harness plus a verifier loop was enough. my coding agent kept re-discovering the same test failure across retries. the loop was working. it just had nowhere to write what it had already learned. here is the decision rule: if your agent forgets across restarts, add write and read. if it stalls on long tasks, add compress. if two sub-agents step on each other, add isolate. architecture without context ops is a well-routed system with amnesia.

12,121 görüntüleme

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Anya Rossi

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Free PRO trial with Kimi-K3, GLM-5.2, Qwen3.8-Max and other strong Alibaba models is live No card needed. Google or temp mail works. What you actually get: • 300 free credits for 14 days • Extra 800 free requests to Qwen3.8-Max (almost 2 months) • Access to Kimi-K3, GLM-5.2 and the rest of the Chinese lineup • Full IDE + CLI + mobile support How to claim it: 1. Open the site and sign in with Google (or temp mail) 2. Download and install their IDE 3. Sign in inside the IDE → confirm → claim the 300-credit offer 4. Go to the Usage tab and grab the extra 800 Qwen3.8-Max requests You can also use the CLI version if you prefer terminal over the IDE. Bonus: they have QoderWork (their version of Claude Design) with modes for: – Slides – Writing / file audit – Landing page design – General chat Models feel responsive and handle files, images and video without drama. One real caveat: the claim is tied to the IDE login, so multi-accounting is harder than the usual website freebies. Still one of the cleanest free ways right now to stress-test the current Chinese frontier models without burning paid credits.

Free PRO trial with Kimi-K3, GLM-5.2, Qwen3.8-Max and other strong Alibaba models is live No card needed. Google or temp mail works. What you actually get: • 300 free credits for 14 days • Extra 800 free requests to Qwen3.8-Max (almost 2 months) • Access to Kimi-K3, GLM-5.2 and the rest of the Chinese lineup • Full IDE + CLI + mobile support How to claim it: 1. Open the site and sign in with Google (or temp mail) 2. Download and install their IDE 3. Sign in inside the IDE → confirm → claim the 300-credit offer 4. Go to the Usage tab and grab the extra 800 Qwen3.8-Max requests You can also use the CLI version if you prefer terminal over the IDE. Bonus: they have QoderWork (their version of Claude Design) with modes for: – Slides – Writing / file audit – Landing page design – General chat Models feel responsive and handle files, images and video without drama. One real caveat: the claim is tied to the IDE login, so multi-accounting is harder than the usual website freebies. Still one of the cleanest free ways right now to stress-test the current Chinese frontier models without burning paid credits.

26,207 görüntüleme • 17 gün önce

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

THIS GUY BUILT AN AUTONOMOUS AI AGENT OUT OF CLAUDE CODE + OBSIDIAN and this is way more interesting than another “use AI to take notes” demo the trick is simple: Obsidian is not the writing app here. it becomes the agent’s memory, task board, and context folder. Claude Code is not just answering prompts. it reads the vault, edits files, follows instructions, and keeps moving through the work like a junior operator with a filesystem. the reusable setup looks like this: 1. create an Obsidian vault for one project 2. keep goals, rules, tasks, decisions, and references as markdown files 3. point Claude Code at the folder 4. give it a clear operating loop: read context → choose next task → execute → write back what changed 5. use the notes as persistent memory instead of re-explaining the project every chat that’s the part people miss. the “agent” is not magic. it’s the boring combination of: - local files - explicit rules - task state - write access - a model that can run through the repo/vault Obsidian makes the memory human-readable. Claude Code makes the memory executable. that combo is why the video worked: it turns a notes app into an operating surface for actual work. best use cases: - content systems - research vaults - coding projects - client ops docs - personal knowledge bases that need actions, not just storage the caveat: if your vault is messy, your agent becomes messy too. folders, naming, “done” criteria, and forbidden actions matter more than the prompt. but once the structure is clean, this is one of the easiest ways to build an agent that remembers what happened yesterday without paying for a full custom app.

30,403 görüntüleme • 2 ay önce

THIS GUY TURNED 5 PROMPTING TIPS INTO A FREE AI CEO CHALLENGE The useful part is treating every prompt like you are briefing a very fast employee who has zero context. Most people open ChatGPT and type a wish. Pros give it a job. Try this instead: 1. Give it a role Not “help me with marketing.” Say: “Act as a B2B SaaS growth operator reviewing a landing page.” 2. Give it the real context Who is the customer? What are they buying? What have you already tried? What does success look like? 3. Give it constraints Length, tone, format, audience, banned words, examples to copy, examples to avoid. A vague prompt gets a vague answer. A constrained prompt gets something you can edit. 4. Ask for options before answers “Give me 5 angles, rank them, then explain the tradeoff.” This turns AI from an autocomplete box into a thinking partner. 5. Force it to show assumptions Before it writes, ask: “What are you assuming, what info is missing, and what would change your answer?” That one line saves a lot of fake confidence. Dan Martell’s video works because the promise is simple: 5 prompting habits that make AI feel less random. The reusable move is even simpler: Stop prompting for outputs. Start prompting for decisions. Bad: “Write me a post.” Better: “Here is the source, here is the reader, here is the angle, give me 3 hooks, choose the strongest, then draft in this style.” That is the difference between getting content-shaped noise and getting work you can actually ship. Caveat: prompts do not fix weak taste, bad data, or unclear strategy. But they do expose those problems faster. If your AI answers are generic, your prompt probably has no job, no context, no constraints, and no standard for what “good” means.

THIS GUY TURNED 5 PROMPTING TIPS INTO A FREE AI CEO CHALLENGE The useful part is treating every prompt like you are briefing a very fast employee who has zero context. Most people open ChatGPT and type a wish. Pros give it a job. Try this instead: 1. Give it a role Not “help me with marketing.” Say: “Act as a B2B SaaS growth operator reviewing a landing page.” 2. Give it the real context Who is the customer? What are they buying? What have you already tried? What does success look like? 3. Give it constraints Length, tone, format, audience, banned words, examples to copy, examples to avoid. A vague prompt gets a vague answer. A constrained prompt gets something you can edit. 4. Ask for options before answers “Give me 5 angles, rank them, then explain the tradeoff.” This turns AI from an autocomplete box into a thinking partner. 5. Force it to show assumptions Before it writes, ask: “What are you assuming, what info is missing, and what would change your answer?” That one line saves a lot of fake confidence. Dan Martell’s video works because the promise is simple: 5 prompting habits that make AI feel less random. The reusable move is even simpler: Stop prompting for outputs. Start prompting for decisions. Bad: “Write me a post.” Better: “Here is the source, here is the reader, here is the angle, give me 3 hooks, choose the strongest, then draft in this style.” That is the difference between getting content-shaped noise and getting work you can actually ship. Caveat: prompts do not fix weak taste, bad data, or unclear strategy. But they do expose those problems faster. If your AI answers are generic, your prompt probably has no job, no context, no constraints, and no standard for what “good” means.

25,573 görüntüleme • 2 ay önce

AgentRouter is handing out $125 in FREE API credits for OPUS 4.8 > No card. > GitHub sign-in. > One API key for Opus 4.8 / Opus 4.7 / Sonnet 4.6 / GLM 5.1 The useful part is not “another model gateway.” It is that you can point coding tools and agents at one OpenAI-compatible base URL instead of juggling separate keys for Claude and GLM, and others. What you get: • $125 signup balance • one API key • OpenAI-compatible endpoints • chat, responses, messages, embeddings, images, audio, rerank endpoints listed • claimed support for Cursor, Claude Code, Hermes, and other agent tools The flow is simple: 1. go to AgentRouter (link in comment) 2. sign in or create an account 3. generate an API key in the console 4. copy the base URL 5. paste it into Cursor / Claude Code / your agent runtime 6. run a small test before moving anything serious Caveat: AgentRouter’s own site currently has a notice saying Claude-series service was recently hit by stability issues and large-scale Claude access was suspended. So don’t treat this as “guaranteed free Claude forever.” Treat it as $125 of free routing credits to test which models are live, how the latency feels, and whether the gateway is stable enough for your workflow. Still a very solid save if you build with agents. Most people burn paid API credits just testing configs. This gives you room to test first, then decide if it belongs in your stack.

AgentRouter is handing out $125 in FREE API credits for OPUS 4.8 > No card. > GitHub sign-in. > One API key for Opus 4.8 / Opus 4.7 / Sonnet 4.6 / GLM 5.1 The useful part is not “another model gateway.” It is that you can point coding tools and agents at one OpenAI-compatible base URL instead of juggling separate keys for Claude and GLM, and others. What you get: • $125 signup balance • one API key • OpenAI-compatible endpoints • chat, responses, messages, embeddings, images, audio, rerank endpoints listed • claimed support for Cursor, Claude Code, Hermes, and other agent tools The flow is simple: 1. go to AgentRouter (link in comment) 2. sign in or create an account 3. generate an API key in the console 4. copy the base URL 5. paste it into Cursor / Claude Code / your agent runtime 6. run a small test before moving anything serious Caveat: AgentRouter’s own site currently has a notice saying Claude-series service was recently hit by stability issues and large-scale Claude access was suspended. So don’t treat this as “guaranteed free Claude forever.” Treat it as $125 of free routing credits to test which models are live, how the latency feels, and whether the gateway is stable enough for your workflow. Still a very solid save if you build with agents. Most people burn paid API credits just testing configs. This gives you room to test first, then decide if it belongs in your stack.

25,316 görüntüleme • 2 ay önce

THIS GUY BUILT A BUSINESS SECOND BRAIN WITH CLAUDE CODE + OBSIDIAN IN 3 STEPS Most teams do not need another Notion workspace. They need a place where the company can remember how it works. The video shows a simple setup: 1. Create one empty folder called second brain. 2. Split it into 3 buckets: raw new knowledge wiki 3. Let Claude Code turn messy company material into connected notes. The useful part is the separation. Raw is where your existing stuff goes: SOPs, sales docs, process notes, client delivery checklists, old Loom summaries, onboarding docs. New knowledge is where fresh outside material lands: articles, clips, tactics, examples, market notes. Wiki is the cleaned version: concepts, roles, processes, SOPs, gaps, reusable decisions. That is where Claude Code becomes more useful than a normal chat window. Instead of asking it to remember random context forever, you give it a folder it can read, edit, and reorganize. Then Obsidian becomes the human interface. The Obsidian Web Clipper captures useful pages into the vault. Claude Code ingests them. The wiki gets updated. Then you can ask questions like: “Does my current workflow actually hold up?” That is the real point. Not “AI notes.” A business memory system that can compare what you do today against new information tomorrow. The caveat: this is not magic company intelligence. If your raw docs are vague, outdated, or full of tribal knowledge, Claude will organize weak inputs into cleaner weak outputs. You still need naming rules, review habits, and someone responsible for deleting junk. But the setup is refreshingly practical. Folder first. Clipper second. Claude Code as the maintainer. No giant knowledge base migration. No complex setup. Just a local vault that can slowly turn scattered business memory into something searchable, editable, and actually reusable.

THIS GUY BUILT A BUSINESS SECOND BRAIN WITH CLAUDE CODE + OBSIDIAN IN 3 STEPS Most teams do not need another Notion workspace. They need a place where the company can remember how it works. The video shows a simple setup: 1. Create one empty folder called second brain. 2. Split it into 3 buckets: raw new knowledge wiki 3. Let Claude Code turn messy company material into connected notes. The useful part is the separation. Raw is where your existing stuff goes: SOPs, sales docs, process notes, client delivery checklists, old Loom summaries, onboarding docs. New knowledge is where fresh outside material lands: articles, clips, tactics, examples, market notes. Wiki is the cleaned version: concepts, roles, processes, SOPs, gaps, reusable decisions. That is where Claude Code becomes more useful than a normal chat window. Instead of asking it to remember random context forever, you give it a folder it can read, edit, and reorganize. Then Obsidian becomes the human interface. The Obsidian Web Clipper captures useful pages into the vault. Claude Code ingests them. The wiki gets updated. Then you can ask questions like: “Does my current workflow actually hold up?” That is the real point. Not “AI notes.” A business memory system that can compare what you do today against new information tomorrow. The caveat: this is not magic company intelligence. If your raw docs are vague, outdated, or full of tribal knowledge, Claude will organize weak inputs into cleaner weak outputs. You still need naming rules, review habits, and someone responsible for deleting junk. But the setup is refreshingly practical. Folder first. Clipper second. Claude Code as the maintainer. No giant knowledge base migration. No complex setup. Just a local vault that can slowly turn scattered business memory into something searchable, editable, and actually reusable.

16,642 görüntüleme • 1 ay önce

THIS GUY BUILT A CLAUDE CODE X OBSIDIAN MAP OF HIS ENTIRE CONTENT SYSTEM This is the useful version of “AI second brain.” Not dumping more notes. Not asking Claude for a prettier folder system. Not making a canvas because it looks smart. In the video, he points Claude Code at his Obsidian setup and shows a visual map of the actual content pipeline: Analysis Ideation Prep Scripting Prep Performance The interesting part is the shape. Each stage is connected to the next one. Some boxes show sub-processes. One section shows a router detecting content type and routing a short into the next step. There are references attached to the flow. That is a real payoff: you stop treating your vault like storage and start treating it like an inspectable machine. The move is simple: 1. Put the real workflow in markdown 2. Let Claude Code inspect the vault 3. Ask it to find stages, dependencies, and missing links 4. Turn the output into an Obsidian map 5. Use the map to see what is manual, duplicated, or broken This works because Claude Code is not just summarizing a note. It can read across prompts, docs, scripts, references, and messy process files, then expose the structure you stopped seeing. That is why the demo hits. The video is not really about “better note-taking.” It is about making your private operating system visible enough to debug. Caveat: a beautiful graph does not mean you have a working system. If the notes are vague, the map will be vague. If the process is fake, Claude will draw a fake process very cleanly. If nothing feeds back into performance, the canvas is just decoration. But if the vault already contains real work, Claude Code x Obsidian becomes a powerful audit tool. Your notes stop being a pile. They become a map of what you actually do.

THIS GUY BUILT A CLAUDE CODE X OBSIDIAN MAP OF HIS ENTIRE CONTENT SYSTEM This is the useful version of “AI second brain.” Not dumping more notes. Not asking Claude for a prettier folder system. Not making a canvas because it looks smart. In the video, he points Claude Code at his Obsidian setup and shows a visual map of the actual content pipeline: Analysis Ideation Prep Scripting Prep Performance The interesting part is the shape. Each stage is connected to the next one. Some boxes show sub-processes. One section shows a router detecting content type and routing a short into the next step. There are references attached to the flow. That is a real payoff: you stop treating your vault like storage and start treating it like an inspectable machine. The move is simple: 1. Put the real workflow in markdown 2. Let Claude Code inspect the vault 3. Ask it to find stages, dependencies, and missing links 4. Turn the output into an Obsidian map 5. Use the map to see what is manual, duplicated, or broken This works because Claude Code is not just summarizing a note. It can read across prompts, docs, scripts, references, and messy process files, then expose the structure you stopped seeing. That is why the demo hits. The video is not really about “better note-taking.” It is about making your private operating system visible enough to debug. Caveat: a beautiful graph does not mean you have a working system. If the notes are vague, the map will be vague. If the process is fake, Claude will draw a fake process very cleanly. If nothing feeds back into performance, the canvas is just decoration. But if the vault already contains real work, Claude Code x Obsidian becomes a powerful audit tool. Your notes stop being a pile. They become a map of what you actually do.

15,941 görüntüleme • 1 ay önce

THIS GUY IS USING GTA 6 TO MAKE $10,000 A MONTH ON THE GAME'S LAUNCH. Not after the game launches. Before it launches. That is the whole play. The creator’s bet is simple: GTA 6 is already a search engine before anyone can play it. Trailers. Scenes. Map theories. Car theories. Release rumors. Money glitch theories. Tiny details people want explained. In the video, he says GTA 6 is projected to make $1B on day one and $7.6B in its first two months. His move is to stand in front of that demand early. The workflow he shows: 1. Take a GTA 6 trailer, scene, rumor, or news angle 2. Ask ChatGPT for a scene-by-scene breakdown 3. Turn that breakdown into YouTube Shorts ideas 4. Paste it into Viewmaxx io for video generation, scriptwriting, and AI voiceover 5. Add captions so the clip still works when people are scrolling fast, muted, or half watching 6. Repeat across every micro-question people search before launch The money claim is the bait. He points to YouTube Shorts paying roughly $2K - $5K per million views, but that is not a guaranteed income plan. RPM depends on niche, country, retention, ad demand, monetization status, and whether the content is original enough to pass platform rules. AI voice + captions + GTA clips is not a business by itself. The useful part is the arbitrage: find a giant upcoming event, break it into small questions, use AI to ship faster than normal editors, and attach each post to demand that already exists. GTA 6 is the example. The reusable model is event-driven Shorts before the event peaks.

THIS GUY IS USING GTA 6 TO MAKE $10,000 A MONTH ON THE GAME'S LAUNCH. Not after the game launches. Before it launches. That is the whole play. The creator’s bet is simple: GTA 6 is already a search engine before anyone can play it. Trailers. Scenes. Map theories. Car theories. Release rumors. Money glitch theories. Tiny details people want explained. In the video, he says GTA 6 is projected to make $1B on day one and $7.6B in its first two months. His move is to stand in front of that demand early. The workflow he shows: 1. Take a GTA 6 trailer, scene, rumor, or news angle 2. Ask ChatGPT for a scene-by-scene breakdown 3. Turn that breakdown into YouTube Shorts ideas 4. Paste it into Viewmaxx io for video generation, scriptwriting, and AI voiceover 5. Add captions so the clip still works when people are scrolling fast, muted, or half watching 6. Repeat across every micro-question people search before launch The money claim is the bait. He points to YouTube Shorts paying roughly $2K - $5K per million views, but that is not a guaranteed income plan. RPM depends on niche, country, retention, ad demand, monetization status, and whether the content is original enough to pass platform rules. AI voice + captions + GTA clips is not a business by itself. The useful part is the arbitrage: find a giant upcoming event, break it into small questions, use AI to ship faster than normal editors, and attach each post to demand that already exists. GTA 6 is the example. The reusable model is event-driven Shorts before the event peaks.

11,134 görüntüleme • 1 ay önce

THIS GUY BUILT A SNACK MACHINE BUSINESS AROUND THE MOST BORING PRODUCT ON EARTH just a simple machine, a location, snacks, and repeatable distribution. that’s why the video works. most people watch it and think: “nice side hustle.” builders should watch it and think: “this is a better product lesson than 90% of startup advice.” because the mechanism is stupidly clear: 1. find a tiny repeatable demand 2. put the offer where the demand already exists 3. remove the human from the transaction 4. restock based on what actually sells 5. repeat only after the unit economics survive reality that last part is the whole game. AI builders keep trying to automate the shiny part first. landing page, prompt chain, avatar video, dashboard, launch post. but the snack machine business starts with something AI people skip: boring proof. can one location pay back? which products move? how often does it need restocking? what breaks? what gets stolen? what happens when nobody cares? this is also why the better AI-UGC businesses are interesting right now. not because “AI makes videos.” because the real workflow is distribution + testing + iteration: multiple accounts, many creatives, fast feedback, then scaling the winners. same idea, different machine. physical vending machine: location → product → purchase → restock data AI content machine: account → creative → attention → revenue data the caveat is obvious: a video can make the machine look cleaner than the business. permits, placement deals, maintenance, theft, dead inventory, and bad locations can kill the margin. but the reusable lesson is still strong: build the smallest cashflow machine you can observe directly. then automate the parts that are already working. not the other way around.

11,646 görüntüleme • 2 ay önce

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